Coding Up a Linear Regression Algorithm From Scratch

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Get started with linear regression, this talk will walk through an advanced Python tutorial in which we will be coding up a Linear Regression algorithm from scratch and making it usable in a manner not so different from sci-kit-learn.
Whether we learn data science through online courses, or tutorials, or jump straight into a hands-on project-based approach, few of us take the time to try and learn how some of our favorite libraries are built. All we know is that we can import the sci-kit-learn library, instantiate a Linear Regression model, and call the “.fit()” method on it. What is happening under the hood? How are these different algorithms implemented in an efficient manner?
We will be learning about Object-Oriented Programming in Python, vectorized operations, and efficient coding strategies that allow for as much future customization as possible. We will be walking through how to convert mathematical formulas into Python code that runs as efficiently as possible.
This webinar is not for the Python beginner, you are expected to know basic Data Science tools and frameworks, such as Pandas, Numpy, Scikit-Learn, etc. You are also expected to know, at least in theory, how Linear Regression works. This webinar will bridge that gap between theory and implementation and in the process teach you some advanced Python tips and tricks.
Presenter Bio:
Asim Waheed holds a bachelor's degree in Computer Science. He is a Data Scientist at Data Science Dojo where he works on building data products while consulting Fortune 500 companies, and course development for online bootcamps as well as corporate training. Before working as a Data Scientist, he was a Research Assistant at the Security and Internet Analytics lab at Lahore University of Management Sciences, working mostly on problems related to Deep Learning Security, and Internet Analytics. He has worked in collaboration with the Machine Learning Security group at Virginia Tech.
Notebook: bit.ly/linear_...
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